The liftOver facilities developed in conjunction with the UCSC browser track
infrastructure are available for transforming data in GRanges formats.
This is illustrated here with an image of the NHGRI GWAS catalog that is,
as of Oct. 31 2014, distributed with coordinates defined by NCBI build hg38.

This lab will walk you through an end-to-end RNA-Seq differential expression workflow,
using DESeq2 along with other Bioconductor packages. We will start from the FASTQ files,
show how these were aligned to the reference genome, prepare gene expression values as
a count matrix by counting the sequenced fragments, perform exploratory data analysis (EDA),
perform differential gene expression analysis with DESeq2, and visually explore the results.

This lab demonstrates how to access data from proteomics data repositories, how to parse
various mass spectrometry data formats, how to identify MS2 spectra and analyse the
search results, how to use the high-level infrastructure for raw mass spectrometry
and quantitative proteomics experiments and quantitative data processing and analysis.

Bioconductor can be used to perform detailed analyses of relationships between
DNA variants and mRNA abundance. Genotype (potentially imputed) and expression
data are organized in packages prior to analysis, using very concise representations.
SNP and probe filters can be specified at run time. Transcriptome-wide testing can be
carried out using multiple levels of concurrency (chromosomes to nodes, genes to
cores is a common approach). Default outputs of the cloud-oriented interface
ciseqByCluster include FDR for all SNP-gene pairs in cis, along with locus-specific
annotations of genetic and genomic contexts.

This workflow describes an analysis pipeline for de novo detection of differential
binding (DB) from ChIP-seq data, from read alignment to interpretation of putative DB regions.
It will be based on the use of sliding windows in the csaw package, with statistical modelling
performed using methods in the edgeR package. Analyses will be demonstrated on real histone
mark and transcription factor ChIP-seq data.

This workflow implements a low-level analysis pipeline for scRNA-seq data using scran,
scater and other Bioconductor packages. It describes how to perform quality control
on the libraries, normalization of cell-specific biases, basic data exploration and
cell cycle phase identification. Procedures to detect highly variable genes,
significantly correlated genes and subpopulation-specific marker genes are also shown.
These analyses are demonstrated on a range of publicly available scRNA-seq data sets.

This workflow demonstrates how to analyse RNA-sequencing data using the edgeR, limma and Glimma packages.
The edgeR package is first used to import, organise, filter and normalise the data,
followed by the limma package with its voom method, linear modelling and empirical
Bayes moderation to assess differential expression and perform gene set testing.
This pipeline is further enhanced by the Glimma package which enables interactive
exploration of the results so that individual samples and genes can be examined by the user.

This workflow elucidates a customizable strategy to identify the effects of technical and
confounding factors on gene expression data and normalize it while preserving the underlying
biological features of interest. The example analysis demonstrated here explores
how certain technical covariates influence the interpretation of the impact of
Coronary Artery Disease on peripheral blood gene expression.

Methylation in the human genome is known to be associated with development and disease.
The Illumina Infinium methylation arrays are by far the most common way to interrogate
methylation across the human genome. This Bioconductor workflow uses multiple packages
for the analysis of methylation array data. Specifically, we demonstrate the steps
involved in a typical differential methylation analysis pipeline including:
quality control, filtering, normalization, data exploration and statistical testing
for probe-wise differential methylation. We further outline other analyses such as
differential methylation of regions, differential variability analysis, estimating
cell type composition and gene ontology testing. Finally, we provide some examples
of how to visualise methylation array data.

Biotechnological advances in sequencing have led to an explosion of publicly
available data via large international consortia such as The Cancer Genome Atlas (TCGA),
The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping
Consortium (Roadmap). These projects have provided unprecedented opportunities to
interrogate the epigenome of cultured cancer cell lines as well as normal and tumor
tissues with high genomic resolution. The Bioconductor project offers more than
1,000 open-source software and statistical packages to analyze high-throughput genomic data.
However, most packages are designed for specific data types (e.g. expression,
epigenetics, genomics) and there is no one comprehensive tool that provides a complete
integrative analysis of the resources and data provided by all three public projects.
A need to create an integration of these different analyses was recently proposed.
In this workflow, we provide a series of biologically focused integrative analyses of
different molecular data. We describe how to download, process and prepare TCGA data
and by harnessing several key Bioconductor packages, we describe how to extract
biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data,
we provide a work plan to identify biologically relevant functional epigenomic elements
associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors:
low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM).